Lecture 10: FinTech Case Studies

1 What’s covered in this lecture?

• Applied AI in Banking and Finance

• Case 1: Financial Index Analysis

  • Review of AIEQ Case

  • Hang Seng Index

• Case 2: Financial Anomoly Detection

  • Outlier Peeling Algorithm

  • 1 Radial Chart Visualization

    class: center, middle

2 Applied AI in Banking and Finance

3 Two FinTech Case Studies

In this lecture, we discuss two FinTech case studies with the equipped DataViz skills:

• Case 1: Financial Index Analysis

  • Review of AIEQ Case

  • Hang Seng Index

• Case 2: Finanial Anomoly Detection

  • Outlier Peeling Algorithm

  • Radial Chart Visualization

4 Investment Stratgies

• Active investing

  • The market is not perfectly efficient

  • Aims to beat the market and take advantage of short-term price fluctuations

• Passive investing

  • The market is efficient in the long run

  • Buy-and-hold philosophy

  • Index tracking strategy, e.g. ETF

5 AIEQ Performance To Date

6 Hang Seng Index Constituents

• X.Shares <- X.Weight * HSI.Close * HSI.Divisor / X.Close

• Data Preprocessing to convert data into space-time format

DataX = read.csv("DataHSI.csv")
DataX$Date2 = as.factor(DataX$Date)
tmp = DataX[!duplicated(DataX$Ticker, fromLast = T),]
idx = order(tmp$Date, tmp$Weight, decreasing = T)
ListTicker=tmp$Ticker[idx]
n = length(ListTicker) 
ListDate = as.character(unique(DataX$Date))
MatWeight <- MatShares <- matrix(NA, length(ListDate), n)
for (j in 1:n){
  TmpX = DataX[DataX$Ticker==ListTicker[j], c("Date2", "Weight", "Shares")]
  MatWeight[as.numeric(TmpX$Date2),j] = TmpX$Weight
  MatShares[as.numeric(TmpX$Date2),j] = TmpX$Shares
}
colnames(MatWeight) <- colnames(MatShares) <- ListTicker
MatWeight = xts(MatWeight, order.by=as.Date(ListDate, "%Y%m%d"))
MatShares = xts(MatShares, order.by=as.Date(ListDate, "%Y%m%d"))

div(dygraph(MatWeight, main = "HSI Constituent Weights",height=450) %>%
      dyRangeSelector(dateWindow = c("2016-01-01", "2018-12-31")) %>%
      dyLegend(show = "never"), align="center")

---

TmpX = MatShares[,1:4]; TmpX = TmpX/rep(1, dim(TmpX)[1])%*%t(colMeans(TmpX, na.rm=T))
div(dygraph(TmpX, main = "HSI Constituent Shares", ylab="Normalized shares",height=450) %>%
      dyRangeSelector(dateWindow = c("2016-01-01", "2018-12-31")) %>%
      dyOptions(colors = c("red", "blue", "green", "cyan"), strokeWidth=2), align="center")

class: center, middle

7 Question of interest:

7.0.1 The AIEQ that tracks S&P500 is based on a semi-passive strategy, can we find its equivalent for Hang Seng index?

class: center, middle

8 Thank you!

Q&A or Email ajzhang@hku.hk。